Performing graph matching can result in simpler, more efficient decision making. For example, graph matching may he performed to simplify a high resolution problem by creating a lower resolution problem that may be easier to solve. Graph matching may also be used to schedule communications over high-speed networks, to compute optimal matrix orderings for numerical linear algebra operations, or for kidney donation pair matching. However, current techniques for performing graph matching have been associated with various limitations.
For example, current implementations for performing graph matching have shown a variety of inefficiencies which may be amplified as data sets utilized during the graph matching increase in size. Furthermore, current implementations may show inefficiencies due to the fact that such implementations may be performed only utilizing serial methodologies. There is thus a need for addressing these and/or other issues associated with the prior art.